Contrasting phenotypes emerging from stable rules: A model based on self-regulated control loops captures the dynamics of shoot extension in contrasting maize phenotypes

Abstract

Background and aims The dynamics of plant architecture is a central aspect of plant and crop models. Most models assume that whole shoot development is orchestrated by the leaf appearance rate, which follows a thermal time schedule. However, leaf appearance actually results from leaf extension and taking it as an input hampers our ability to understand shoot construction. The objective of this work was to assess a modelling framework for grasses, in which the emergence of leaves and other organs is explicitly calculated as a result of their extension. Methods The approach builds on the model of Zhu et al. (2014), which uses a set of rules co-ordinating the timing of development within and between phytomers. We first assessed rule validity for four experimental data sets, including different cultivars, planting densities and environments and accordingly revised the equations driving the extension of the upper leaves and of internodes. We then fitted model parameters for each data set and evaluated the ability to simulate the measured phenotypes across time. Finally, we carried out a sensitivity analysis to identify the most impacting parameters and investigate model behaviour. Key results The modified version of the model simulated correctly the contrasting maize phenotypes. Co-ordination rules accounted for the observations in all studied cultivars. Factors with major impact on model output included the extension rates, the time of tassel initiation and initial conditions. A large diversity of phenotypes could be simulated. Conclusions This work brought direct experimental evidence for co-ordination rules and illustrated the capacity of the model to represent contrasting phenotypes. These rules play an important role in patterning shoot architecture and some of them should be further assessed, considering contrasting growth conditions. To make the model more predictive, several parameters could be considered in the future as internal variables driven by plant status.

Two maize cultivars of contrasting leaf size show different leaf elongation rates with identical patterns of extension dynamics and coordination

“…The pattern of RER shown here markedly differs from those reported in Vidal et al (2020) , which showed a plateau or a slow decrease for phytomers above the ear. The experimental protocol and data analysis were the same in this study and in Vidal et al (2020) ; therefore, the differences observed represent variations of plant behaviour relating to genotype and/or environmental conditions.…”

“…In such models, the parameters of leaf extension are adjusted so that the emergence follows a predetermined timetable. In a few models ( Zhu et al 2014 ; Gauthier et al 2020 ; Vidal et al 2020 ), the emergence of leaf tips and ligules is calculated according to the organ extension compared with the dimension of older organs, so that the phyllochron and phases of leaf extension are dynamically regulated and depend on leaf initiations and growth rates. The latter approach brings the promise of understanding how patterns in size and time emerge from extension and coordination.…”

“…In maize, the coordination between leaf appearance/emergence and sheath development holds only for juvenile leaves. The sheaths of upper leaves are initiated at a higher rate than lower leaves, which results in a decreasing duration of the blade extension with higher leaf position above the ear, and finally contributes to the progressive decrease in the blade size towards the top of the shoot ( Andrieu et al 2006 ; Zhu et al 2014 ; Vidal et al 2020 ). While a change in the rate of sheath development is well acknowledged, data describing sheath initiation in upper leaves are limited and of low accuracy.…”

“…Only a limited amount of data have been published that describe the pattern of RER or LER along a grass shoot. For maize, most of these data have been recently reviewed by Vidal et al (2020) .…”

A new hypothesis-testing model for phyllochron based on a stochastic process - application to analysis of genetic and environment effects in maize

“…Thus, the flowering time is tightly dependent on the duration of the vegetative phase, which is determined by the number of leaves to initiate from the meristem and by their rate of initiation (Padilla and Otegui, 2005). Maize (Zea mays) is a monocotyledon species that initiates between seven to more than 18 phytomers before flowering (Flint-Garcia et al, 2005;Vidal and Andrieu, 2019). Observation of the time between successive leaves initiation (plastochron) requires destructive measurements and cannot be repeatedly performed on individual plants.…”